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Mapping technology diffusion with AI: a web-based approach for tracking additive manufacturing adoption


Schwierzy, Julian ; Dehghan, Robert ; Schmidt, Sebastian ; Grashof, Nils ; Hottenrott, Hanna ; Woywode, Michael


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DOI: https://doi.org/10.1016/j.jjimei.2025.100387
URL: https://www.sciencedirect.com/science/article/pii/...
Additional URL: https://www.researchgate.net/publication/399499520...
URN: urn:nbn:de:bsz:180-madoc-717111
Document Type: Article
Year of publication: 2026
The title of a journal, publication series: International Journal of Information Management Data Insights
Volume: 6
Issue number: 1, Article 100387
Page range: 1-11
Place of publication: Cheltenham
Publishing house: Elsevier
ISSN: 2667-0968
Publication language: English
Institution: Business School > Mittelstandsforschung u. Entrepreneurship (Woywode 2007-)
Außerfakultäre Einrichtungen > Institut für Mittelstandsforschung (ifm)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 330 Economics
Classification: JEL: L11 , L22 , L23 , O33,
Keywords (English): technology adoption , additive manufacturing , diffusion , web-based indicator , 3D printing
Abstract: Understanding the diffusion of emerging technologies is essential for capturing the benefits of innovation. Yet, traditional science, technology, and innovation (ST&I) indicators are often limited in measuring technology adoption. This study investigates the potential of analyzing corporate websites through web mining and machine learning to measure the adoption of additive manufacturing (AM) technologies. Furthermore, it examines how regional ST&I indicators — specifically patents and publications — shape AM adoption patterns. Despite still being niche, AM adoption in Germany doubled from 0.37% (2022) to 0.74% (2023) of firms. Regional web-based adoption hot spots largely align with patent and publication activity. In addition, our regression analyses reveal a positive and statistically significant relationship between these indicators and AM diffusion based on our AI-based web indicator. These results underline the potential of WebAI methods to complement traditional ST&I indicators.


SDG 9: Industry, Innovation and Infrastructure


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BASE: Schwierzy, Julian ; Dehghan, Robert ; Schmidt, Sebastian ; Grashof, Nils ; Hottenrott, Hanna ; Woywode, Michael

Google Scholar: Schwierzy, Julian ; Dehghan, Robert ; Schmidt, Sebastian ; Grashof, Nils ; Hottenrott, Hanna ; Woywode, Michael

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